HIV vaccine research is an essential part of the fight against the global HIV/AIDS pandemic. Finding immune responses that predict vaccine-induced protection, or immune correlates, has a great impact on the HIV vaccine research, as it sheds light on the mechanism of the protection and helps improve vaccine design. This proposal is composed of two sets of statistical methods for improving the statistical power of identifying immune correlates. Aim 1 develops methods that will help investigators choose the best immune response positivity criteria. A positivity criterion for an immune response is a set of rules that classifies a subject's response as either negative or positive. A good positivity criteron can be used to help define immune response variables to assess their correlation with infection status. The proposal takes a decision theoretic approach to choosing positivity criteria that balance the false positive rates and the false negative rates. Aim 2 develops analytical methods for an important technology used to measure immune responses: the multiplex bead array (MBA). A key step in processing MBA data is to fit a concentration-response curve using data from standard samples which contain known amounts of analytes to be measured. Poor curve fits lead to large measurement error, which will dampen the statistical association between the infection status and the observed immune responses. The proposal examines two issues in curve fitting. First, two approaches for handling the mean-variance relationship of the observed fluorescence intensity are compared: weighted least squares and un-weighted least squares on log-transformed fluorescence intensity. Second, three models for the shape of the concentration-response curves are compared: four-parameter logistic model, five-parameter logistic model, and a semi-parametric model.